Turn clinical documents into structured data.
DocLD helps payers, providers, and health tech teams process high-volume, multi-format documents where precision matters. Parse clinical notes, lab reports, claims, and medical records with one API — layout-aware extraction, OCR for scans, and citation-ready chunks for RAG and compliance.
Clinical notes and EMR/EHR
Discharge summaries, progress notes, and unstructured clinical text.
Clinical documentation lives in discharge summaries, progress notes, and EMR exports where structure varies by system and provider. DocLD parses PDFs and documents with layout-aware extraction so you get clean text and structure — ready for semantic chunking and retrieval. Build RAG and agent flows that cite back to the exact note or section for clinical decision support and summarization.
Parse returns chunks with page and bounding-box context so your pipelines stay traceable and audit-ready.
Lab results and imaging reports
PDF and image reports with values, reference ranges, and dates.
Lab results and radiology reports often arrive as PDFs or scanned images with tables, reference ranges, and critical values. DocLD supports PDF and image parsing with table extraction and optional agentic OCR, so you get structured data — values, units, dates — for clinical workflows, quality reporting, and research.
Use the Extract API with schemas to pull specific fields (e.g. lab codes, results, dates) with citations back to the source.
Insurance and claims
Prior auth, EOBs, and claim forms in mixed formats.
Prior authorization forms, explanation of benefits, and claim documents come as PDFs, scans, and sometimes handwritten fields. DocLD uses VLM-based OCR with 50+ languages and table extraction so you can ingest mixed-format documents through one API. Extract structured fields for eligibility checks, adjudication logic, and downstream systems.
Run parsing and extraction via API in your own environment, with configurable presets and webhooks for batch intake.
Consent and regulatory
Consent forms and regulatory submissions need traceable outputs.
Consent forms, regulatory submissions, and compliance documentation require linking every output back to its source. DocLD Parse returns chunks with page ranges and optional bounding boxes; Extract can pull structured fields with citations. Build RAG and agent flows that show where each answer came from for audit trails and regulatory review.
Use the same API for PDFs, images, and Office documents — no format-specific integrations required.
Long medical records and batch
Multi-hundred-page records and high-volume intake.
Medical records and chart requests can run to hundreds of pages, with data that must be preserved and retrievable. DocLD supports files up to 100MB with semantic, fixed-size, or page-based chunking so you can tune for RAG quality and context windows.
Use the async Parse endpoint and webhooks for large documents and batch jobs — no need to block on synchronous responses when processing record requests or migration batches.
How teams use DocLD in healthcare
| Use case | Description |
|---|---|
| Prior authorization and eligibility | Parse prior auth forms, clinical documentation, and eligibility responses for faster review and decisioning. |
| Claims and EOB processing | Extract data from EOBs, claim forms, and remittance advice; push structured data into adjudication and reporting. |
| Clinical summarization and RAG | Turn unstructured notes and reports into a searchable knowledge base with citation-backed answers for care teams. |
| Research and real-world evidence | Ingest published literature, trial documents, and real-world data sources for analysis and evidence synthesis. |
| Patient records intake and migration | Parse legacy charts, scanned records, and external documents for intake, migration, and interoperability workflows. |
| Consent and compliance documentation | Extract and structure consent forms, regulatory submissions, and audit trails with source citations. |
Healthcare: Questions & Answers
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